Improving A Deep Neural Network Generative-Based Chatbot Model

• Main Authors: Wan Solehah, Wan Ahmad, Mohamad Nazim, Jambli
• Format: Article
• Language: English
• Published: Penerbit UTM Press 2024
• Subjects: Q Science (General); T Technology (General)
• Online Access: http://ir.unimas.my/id/eprint/45008/2/IMPROVINGADEEPNEURAL.pdf; http://ir.unimas.my/id/eprint/45008/; https://journals.utm.my/aej/article/view/20663; https://doi.org/10.11113/aej.v14.20663
• Institution: Universiti Malaysia Sarawak

A chatbot is an application that is developed in the field of machine learning, which has become a hot topic of research in recent years. The majority of today's chatbots integrate the Artificial Neural Network (ANN) approach with a Deep Learning environment, which results in a new generation chatbot known as a Generative-Based Chatbot. The current chatbot application mostly fails to recognize the optimum capacity of the network environment due to its complex nature resulting in low accuracy and loss rate. In this paper, we aim to conduct an experiment in evaluating the performance of chatbot model when manipulating the selected hyperparameters that can greatly contribute to the well-performed model without modifying any major structures and algorithms in the model. The experiment involves training two models, which are the Attentive Sequence-to-Sequence model (baseline model), and Attentive Seq2Sequence with Hyperparametric Optimization. The result was observed by training two models on Cornell Movie-Dialogue Corpus, run by using 10 epochs. The comparison shows that after optimization, the model’s accuracy and loss rate were 87% and 0.51%, respectively, compared to the results before optimizing the network (79% accuracy and 1.05% loss).